Self-correction method for shooting data of antiaircraft gun

Abstract

The invention discloses an antiaircraft gun firing data self-correction method. The method comprises the following steps: A, acquiring environment data and target data; b, fitting a target motion track according to the target data; c, after a hit position is determined according to the target motion trail, firing data are calculated according to the hit position and the environment data; d, the projectile is launched according to the firing data calculated in the step C, and the actual initial speed, the projectile target position deviation and the projectile target time deviation of the projectile are collected; and E, the actual initial velocity of the projectile is used as the calculated value of the initial velocity of the next projectile launching, and the firing data are corrected through the projectile target position deviation and the projectile target time deviation. According to the method, the defects in the prior art can be overcome, and the correction precision of the firing data in a scene with obvious environmental parameters can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire control systems, and in particular to a method for self-correcting the firing data of anti-aircraft guns. Background Art

[0002] The firing data of anti-aircraft guns is a set of parameters for anti-aircraft gun shooting. Due to numerous factors affecting the shooting accuracy of anti-aircraft guns, it is necessary to correct the firing data during the shooting process. Early fire control systems used mechanical analog computing devices to obtain target information through optical sights and stereoscopic rangefinders, combined with manual correction of parameters such as air pressure and wind speed to generate preliminary firing commands. Such systems are limited by mechanical accuracy and manual operation efficiency, and have insufficient response speed and accuracy to targets. In recent years, with the popular application of intelligent algorithms, methods using intelligent algorithms such as genetic algorithms to adaptively correct the firing data have gradually increased, improving the efficiency of data feedback processing. However, the convergence speed of the genetic algorithm is relatively low, and its adaptability to dynamic environments is poor, which leads to insufficient iteration to ensure the timeliness of data output and insufficient correction accuracy in scenarios where environmental parameters change significantly. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for self-correcting the firing data of anti-aircraft guns, which can solve the deficiencies of the prior art and improve the correction accuracy of the firing data in scenarios where environmental parameters change significantly.

[0004] To solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0005] A method for self-correcting the firing data of anti-aircraft guns, characterized by comprising the following steps:

[0006] A. Obtain environmental data and target data;

[0007] B. Fit the target motion trajectory according to the target data;

[0008] C. After determining the hit position according to the target motion trajectory, calculate the firing data according to the hit position and environmental data;

[0009] D. Fire projectiles according to the firing data calculated in step C, and collect the actual initial velocity, projectile-target position deviation, and projectile-target time deviation of the projectiles;

[0010] E. Use the actual initial velocity of the current projectile as the calculated value of the initial velocity for the next projectile launch, and correct the firing data using the projectile-target position deviation and projectile-target time deviation.

[0011] Preferably, the environmental data includes air humidity, air temperature, air density, wind speed, and wind direction; the target data includes target motion speed and target motion acceleration.

[0012] Preferably, the target motion trajectory is s(t) = s0 + ∫0 t (v0 + ∫0 t′ a(t″)dt″)dt′, where S0 is the target initial position, t is the motion time, V0 is the target initial velocity, and a(t) is the target's real-time acceleration.

[0013] Preferably, the firing elements include the horizontal firing angle and the elevation angle. The horizontal firing angle The elevation angle where Δx and Δy are the deviations in the x and y directions on the horizontal plane between the antiaircraft gun position and the target respectively, g is the acceleration due to gravity, d is the horizontal range, and v is the initial muzzle velocity of the projectile.

[0014] Preferably, the firing elements are corrected using environmental data, including the following steps,

[0015] The correction amount of the initial muzzle velocity of the projectile ΔV = V × η × (T 药 - T 基准 );

[0016] The correction amount of the horizontal range of the projectile

[0017] where η is the muzzle temperature sensitivity coefficient, T 药 is the real-time muzzle temperature, T 基准 is the reference muzzle temperature, K ρ is the ballistic coefficient, ρ is the actual air density, ρ0 is the standard air density, ΔT is the difference between the actual temperature and the virtual temperature T v , the virtual temperature T is the real-time air temperature, e is the water vapor pressure, and p is the atmospheric pressure.

[0018] Preferably, a genetic algorithm model is used to correct the firing elements; the current firing elements and historical firing elements are used to generate an initial population, and the optimization goal is set to minimize the sum of the absolute value of the projectile-target position deviation and the absolute value of the projectile-target time deviation. The fitness function is where E is the expected value of the objective function of the environmental random variable ζ under the probability distribution p(ζ), ΔT is the projectile-target time deviation, ΔS is the projectile-target position deviation, λ is the penalty factor, x is the firing element to be optimized, and Penalty(x) is the penalty function; individuals to be retained are selected according to the magnitude of the fitness function, and then crossover and mutation operations are performed on the retained individuals to obtain a new population. The fitness of the individuals in the new population is calculated. If the fitness is greater than the set threshold or the number of iterations reaches the preset upper limit, the optimization is stopped, and the individual with the maximum fitness is taken as the output of the genetic algorithm model to obtain the corrected value of the firing elements.

[0019] Preferably, when performing a selection operation on individuals, a dynamic weight is assigned to each individual. where θ is the selection coefficient, and the adaptive iteration method of θ is D is the diversity index of the current population. Set the minimum distance between selected individuals, and the distances between selected individuals are all greater than the set minimum distance. Set a selection ratio of 3% of low-fitness individuals. Each time of selection, randomly use no less than 3% of the individuals among the selected individuals to replace the unselected individuals.

[0020] Preferably, when performing a mutation operation on individuals, specific mutations are performed according to the change of the environmental random variable; a part of the individuals are selected for specific mutations at a set ratio, and the mutation process is where x is the individual of specific mutation, a and b are coefficients conforming to the standard normal distribution, and ΔE is the deviation of the expected value of the environmental random variable between two adjacent iterations. is a random vector, and ε is the adaptive step factor.

[0021] Preferably, for the individuals undergoing specific mutations, the selection coefficient θ in their next iteration process is updated. θ ’ is the updated selection coefficient.

[0022] The beneficial effects brought by adopting the above technical solutions are as follows: The present invention performs second-order correction on the firing elements by using environmental parameters and a genetic algorithm model, improving the correction efficiency and the fitness to the change of environmental parameters. Description of the Drawings

[0023] Figure 1 is a flowchart of a specific embodiment of the present invention. Specific Embodiment

[0024] I. Calculate the initial value of the firing elements

[0025] Use a ground meteorological station to collect environmental parameters such as air humidity, air temperature, air density, wind speed, and wind direction. Use a radar system to obtain the initial state vector of the target, predict the target state through Kalman filtering (KF), and accurately measure the target state within the prediction range. Fit the target trajectory segment by segment through the interacting multiple model (IMM) algorithm, and then introduce particle filtering (PF) to correct the non-linear trajectory. The target motion trajectory is s(t) = s0 + ∫0 t (v0 + ∫0 t′ a(t″)dt″)dt′, where S0 is the initial position of the target, t is the motion time, V0 is the initial velocity of the target, and a(t) is the real-time acceleration of the target.

[0026] Calculating firing data based on the impact position and environmental data is a complex process that integrates ballistics, environmental science, and optimization algorithms. The core lies in establishing the mapping relationship between environmental disturbances and ballistic deviations through reverse engineering, and dynamically correcting the launch parameters to achieve precise strikes.

[0027] The ballistic equation adopts a six-degree-of-freedom kinematic model, and the longitudinal motion equation is The normal motion equation is Where X is the aerodynamic drag, Y is the lift, and P is the air pressure. The firing table calculation uses the cubic spline interpolation method S(x) = a j +b i (x - x i ) + c i (x - x i ) 2 +d i (x - x j ) 3 Generate a continuous ballistic curve through discrete data points to ensure an accurate firing angle corresponding to any range. When the fire control system performs real-time calculation, iteratively solve the bullet-target intersection equation Use the Newton iteration method to make the motion trajectories of the projectile and the target converge in the space-time domain.

[0028] After determining the impact position based on the target motion trajectory, calculate the firing data according to the impact position and environmental data. The horizontal firing angle The firing elevation angle Where Δx and Δy are the deviations in the x-direction and y-direction on the horizontal plane between the antiaircraft gun position and the target respectively, g is the acceleration due to gravity, d is the horizontal range, and v is the initial firing velocity of the projectile.

[0029] II. Fire the projectile and monitor the projectile trajectory

[0030] The radar measures the instantaneous velocity of the projectile using the Doppler effect, and calculates the bullet-target distance and azimuth angle in combination with the pulse echo time difference. The optical tracker captures the projectile's tail flame or tracer signal through a high-speed imaging system, uses image processing algorithms to real-time calculate the two-dimensional coordinates of the projectile in the image plane, and reconstructs the three-dimensional trajectory through bistatic or multistatic intersection measurement.

[0031] III. Firing data correction

[0032] (1) Correct the firing data using environmental data

[0033] The correction amount of the projectile's initial firing velocity ΔV = V × η × (T 药 -T 基准 );

[0034] The correction amount of the projectile's horizontal range

[0035] where η is the drug temperature sensitivity coefficient, T 药 is the real-time drug temperature, T 基准 is the reference drug temperature, K ρ is the ballistic coefficient, ρ is the actual air density, ρ0 is the standard air density, ΔT is the difference between the actual temperature and the virtual temperature T v , and the virtual temperature T is the real-time air temperature, e is the water vapor pressure, and p is the atmospheric pressure.

[0036] (2) Use the genetic algorithm model to correct the firing data

[0037] Generate an initial population using the current firing data and historical firing data. The optimization goal is set to minimize the sum of the absolute value of the projectile-target position deviation and the absolute value of the projectile-target time deviation. The fitness function is where E is the expected value of the objective function of the environmental random variable ζ under the probability distribution p(ζ), ΔT is the projectile-target time deviation, ΔS is the projectile-target position deviation, λ is the penalty factor, x is the firing data to be optimized, and Penalty(x) is the penalty function. Select the individuals to be retained according to the magnitude of the fitness function, and then perform crossover and mutation operations on the retained individuals to obtain a new population. Calculate the fitness of the individuals in the new population. If the fitness is greater than the set threshold or the number of iterations reaches the preset upper limit, stop the optimization and take the individual with the maximum fitness as the output of the genetic algorithm model to obtain the corrected value of the firing data.

[0038] To further improve the convergence speed of the genetic algorithm model and reduce the probability of falling into a local optimal solution, we introduce two concepts: "individual dynamic weight" and "low fitness individual selection ratio". When performing the selection operation on individuals, assign a dynamic weight to each individual where θ is the selection coefficient, and the adaptive iteration method of θ is D is the diversity index of the current population. Set the minimum distance between the selected individuals. The distances between the selected individuals are all greater than the set minimum distance. Set a 3% low fitness individual selection ratio. Each time of selection, randomly use no less than 3% of the individuals among the selected individuals to replace the unselected individuals. By optimizing the selection process, the diversity of the new population after selection can be expanded while maintaining the relative stability of the new population.

[0039] Since the environmental parameters are in dynamic change, the traditional genetic algorithm cannot respond in a timely manner to the changes in environmental parameters during the optimization iteration process, which leads to the setting of the genetic algorithm model parameters lagging behind the changing state of the environment. To address this issue, we introduce the concept of "specific mutation" in the mutation process of the genetic algorithm. When performing the mutation operation on individuals, perform specific mutation according to the changes in environmental random variables; select a part of the individuals for specific mutation at a set ratio, and the mutation process is Where x is a specific variant individual, a and b are coefficients conforming to the standard normal distribution, ΔE is the deviation of the expected value of the environmental random variable in two adjacent iteration processes, is a random vector, and ε is an adaptive step factor. Since the process of specific variation is related to the deviation of the expected value of the environmental random variable, spatio-temporal decoupling can be achieved through the specific variation of individuals, realizing prospective variation triggering to compensate for the lag of the model to environmental parameter changes. At the same time, since the diversity of the new population is optimized in the individual selection process, the variation triggering window can be enlarged when performing specific variation on the new population, improving the targeting of variation.

[0040] Using the PGZ-09 type 35 mm self-propelled anti-aircraft gun as the simulation object for simulation experiments, the range of the simulation experiment environmental parameters (the environmental parameters vary randomly during the simulation process) is temperature 18 - 20 °C, humidity 20 - 45%, air density 92 - 95% of the standard density, wind speed 5 - 8 m / s, target flight altitude 500 m, flight speed 50 - 60 m / s, flight angle forming a 35° angle with the wind direction, and the closest distance between the target and the anti-aircraft gun during the target flight is 1000 m. Three rounds of shooting simulations are carried out, 100 rounds per round, the firing rate is 50 rounds / min, and the average shooting errors of each round are shown in the following table.

[0041] Missile-target position deviation (m) Missile-target time deviation (ms) The first round 0.73 165 The second round 0.81 172 The third round 0.75 160

[0042] It can be seen from the above simulation experiments that the anti-aircraft gun firing data self-correction method provided by the present invention can enable the anti-aircraft gun to achieve stable and high-precision continuous shooting.

[0043] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0044] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for self-correcting the firing data of an anti-aircraft gun, characterized in that It includes the following steps: A. Obtain environmental data and target element data; B. Fit the target motion trajectory according to the target element data; C. After determining the hit position according to the target motion trajectory, calculate the firing elements based on the hit position and environmental data; D. Fire projectiles according to the firing elements calculated in step C, and collect the actual initial velocity, projectile-target position deviation, and projectile-target time deviation of the projectiles; E. Use the actual initial velocity of the current projectile as the calculated value of the initial velocity for the next projectile launch, and correct the firing elements using the projectile-target position deviation and projectile-target time deviation.

2. The method for self-correcting the firing data of an anti-aircraft gun according to claim 1, characterized in that: The environmental data includes air humidity, air temperature, air density, wind speed, and wind direction; the target element data includes target motion speed and target motion acceleration.

3. The method for self-correcting the firing data of an anti-aircraft gun according to claim 2, characterized in that: The target motion trajectory is where S0 is the initial position of the target, t is the motion time, V0 is the initial velocity of the target, and a(t) is the real-time acceleration of the target.

4. The method for self-correcting the firing data of an anti-aircraft gun according to claim 2, wherein: The firing data includes the horizontal firing angle and the elevation angle. The horizontal firing angle The elevation angle where Δx and Δy are the deviations in the x - direction and y - direction on the horizontal plane between the position of the antiaircraft gun and the target respectively, g is the acceleration due to gravity, d is the horizontal range, and v is the initial velocity of the projectile firing.

5. The method for self-correcting the firing data of an anti-aircraft gun according to claim 4, characterized in that: Using the environmental data to correct the firing elements includes the following steps The initial velocity correction amount ΔV of the projectile shooting is ΔV = V×η×(T 药 - T 基准 ); Horizontal range correction amount of projectile Among them, η is the drug temperature sensitivity coefficient, T 药 is the real-time drug temperature, T 基准 is the reference drug temperature, K ρ is the ballistic coefficient, ρ is the actual air density, ρ0 is the standard air density, ΔT is the difference between the actual temperature and the virtual temperature T v , the virtual temperature T is the real-time air temperature, e is the water vapor pressure, and p is the atmospheric pressure.

6. The method for self-correcting the firing data of an anti-aircraft gun according to claim 5, characterized in that: Use the genetic algorithm model to correct the firing elements; generate the initial population using the current firing elements and historical firing elements, with the optimization objective set to minimize the sum of the absolute value of the projectile-target position deviation and the absolute value of the projectile-target time, and the fitness function is where \(E\) is the expected value of the objective function of the environmental random variable \(\zeta\) under the probability distribution \(p(\zeta)\), \(\Delta T\) is the projectile-target time deviation, \(\Delta S\) is the projectile-target position deviation, \(\lambda\) is the penalty factor, \(x\) is the firing elements to be optimized, and \(Penalty(x)\) is the penalty function; select the individuals to be retained according to the magnitude of the fitness function, then perform crossover and mutation operations on the retained individuals to obtain a new population, calculate the fitness of the individuals in the new population, and if the fitness is greater than the set threshold or the number of iterations reaches the preset upper limit, stop the optimization, and take the individual with the maximum fitness as the output of the genetic algorithm model to obtain the corrected value of the firing elements.

7. The method for self-correcting the firing data of an anti-aircraft gun according to claim 6, characterized in that: When performing a selection operation on individuals, a dynamic weight is assigned to each individual where θ is the selection coefficient, and the adaptive iteration method of θ is D is the diversity index of the current population. Set the minimum distance between selected individuals. The distances between selected individuals are all greater than the set minimum distance. Set a selection ratio of 3% for low fitness individuals. Each time of selection, randomly use no less than 3% of the individuals among the selected individuals to replace the unselected individuals 8. The method for self-correcting the firing data of an anti-aircraft gun according to claim 7, characterized in that: When performing mutation operations on individuals, specific mutations are carried out according to the changes in environmental random variables; a part of the individuals are selected for specific mutations at a set ratio, and the mutation process is where x is the individual with specific mutation, a and b are coefficients conforming to the standard normal distribution, and ΔE is the deviation of the expected value of the environmental random variable in two adjacent iteration processes, is a random vector, and ε is an adaptive step size factor.

9. The method for self-correcting the firing data of an anti-aircraft gun according to claim 8, characterized in that: For an individual undergoing specific mutations, update the selection coefficient θ in its next iteration process. θ ’ is the updated selection coefficient.

Images